This article originally appeared in Finance & Gestion edition 361, the publication of the DFCG (the French National Association of Management and Financial Directors) and has been translated and published with permission. The article was originally written by Martial Rouyère, Directeur, and Laurent Dugas, Partner, at P-VAL Conseil. P-VAL Conseil is an Yseop partner and a consultancy firm focused on digital transformation in financial services.

AI is a significant area of consideration for management teams in the Finance and Banking sectors. It has potential to optimise processes, both in terms of quality and quantity, and also offers a unique opportunity to develop additional skills within your teams, encouraging engagement and employee retention.

AI has great potential to make your financial processes more efficient. Not only can you reduce the time spent on tedious and drawn out tasks, but also use it to optimise your team. Think about all you would like to do to help your operational managers: polishing your financial communications, optimising investments, and working on more diverse projects.

Using AI your advisors have more time to perform high value tasks, such as having time to add their analysis to a report or suggest better ways of working.

The AI revolution isn’t just seeing quantitative improvements, but also is leading to improvements in quality and value on many levels. Banking and Finance roles are evolving into three profiles.

Front Office / Customer Facing Roles

These are the business partners who help managers in analysis and decision making. In one click they can generate reports in natural language which clearly explain changes and discrepancies. The AI technology generates these reports by applying the business rules that would previously have been used in manual data analysis and report writing. Going from this shared base of understanding, shared with the manager, they can then invest time in building a strategy to allow them to stay ahead of the competition.

Expert Advisors

Today the term ‘Expert’ is rather subjective, and sometimes a little deceptive, but with AI, your experts have all the data they need at hand, in a form they can leverage to help their clients. They can tackle problem head-on, making the links between financial data, and non-financial data such as customer satisfaction to create clear strategies and models for growth and success.

Financial Management

In the sphere of financial management and regulation, AI makes it possible to create accurate models, helping in the detection of risk and fraud.

The study “Intelligence Artificial for Finance” published by l’Association des Directeurs Financiers et du Contrôle de Gestion (DFCG) in April 2018, highlights several important pathways opened up by AI. Here we will look at how the automation of management reporting is transforming the function of the Business Partner.

Formally setting down the business rules and decision-making logic of your top analysts into an AI system allows for reports to be generated in one click. In a step away from the huge effort required previously to work out the figures and write a report at last minute, from now on the team can make the most of the time saved to assure the quality of the reporting and analysis, generating reports earlier in the process. The team can rely on this first round of analysis to delve into the details for deeper analysis, working closely with the operational team, and staying ahead of deadlines.

Since the start of 2017, Pval have deployed many “Intelligent reporting” projects, such as cost analysis of different support functions, risk analysis in line with IFRS and credit analysis, to name but a few examples. Here we’ll share our process for integrating AI technology within the teams we work with.

These projects normally take around six months, from the launch date until they are up and running.

They generate a good return on investment, which is even greater when the report is used frequently, for multiple entities and by a whole team. To give an idea, having this AI project in place reduces report production costs by 10 to 20%, before even considering the benefits of a quicker production time and higher quality consistent reporting and decision making.

Step One: Clarify expected outcomes and define reporting priorities

An important first step is to make sure that the project isn’t just seen as another IT system, but as a performance tool.

What is the performance you are looking to achieve? For a project on general costs, the value is to strengthen the “business partner” role of your team, while for a project focusing on the risk analysis, the value is to give an expert opinion, explaining not only the data findings, but also explaining the potential impact of different choices.

Once we have identified the aim of the project, it’s time to work out which current reports are most suitable to transform with AI, considering their current time and cost of production, the complexity of analysis, the volume of data to analyse, how well the report can be standardised and where we can see quality improvements.

The availability and security of data is the deciding factor, something that must be taken into consideration right from the from the start of the project, to ensure that the requirements for IT to exchange data aren’t beyond the capabilities of the client’s organisation.

The performance equation has three other success factors.

the extent to which we allow the user to adapt the report.

the purpose of the report, recorded in the process that has an impact on multiple clients. For example, the IFRS9 report that is used at different times by the CEO, Finance Director and the Directors of Risk.

the collective way of thinking and acting, a cultural change. At each level of the business people need to adapt to this new way of working.

From the first scoping workshops with the teams we contrast the current ways of working with the vision of how things will be. In this way, AI, becomes the pathway for change and for the evolution of the profession. The goal isn’t simply to implement AI as an end in itself, or just to cut costs or keep with trends.

Step Two: Deliver the project with the key people

Using an agile methodology allows us to work closely with collaborators and adapt the project as we go along. We work closely with our clients’ teams to tease out how exactly they compile and use the reports. Instead of putting them on the spot with direct questions, whose answers are difficult to come to such as “what are your analysis rules for this type of report?”, we instead discuss the usage of the report. We take time to understand their day to day processes and build up a full picture of their work together with questions such as: “What do you do?”, “How is it read?”, “What in it is important?”

Yseop’s AI system produces a first draft of the report, which is enriched over the course of workshops as we work on a turn of phrase that is clear to everyone. As always continuing the discussion is at the heart of what we do: “I wouldn’t have said it like that”, “what would you like to read when it’s published”, “In reality, we need to take this variable into account”.

As they discover more about the possibilities of AI the teams come up with more business rules, and ways to tailor the analysis. The active involvement of the business experts throughout this process is essential in producing a successful solution the goes beyond being a simple reproduction of the original report.

Step Three: put in place the transformation of the profession – new ways of working, and new aims.

Over the course of the workshops we work with the teams on how they can use their new potentials, in particular the time that will be freed up in through the automation of report writing. How best can they use this newfound capacity? What would most benefit their clients and colleagues? How should their days now be structured? For some, the change in their daily role is a significant disruption. From the start of the project the management underlines this sense of evolution, while outlining the new duties.

On one of our projects on general costs, we needed to define a whole new way of working for team, who for the first time would have the time to interact with other business units.

Step Four: Scale and cement a sustainable performance

Standardising the projects is what really delivers the return on investment for the teams we work with. In order to bring immediate value, we think of industrialisation in a series of stages. The first is to create a pilot that the team can use. This is made using information from your company, which plays a big role in the project form the outset. This can be extended to create a whole family of reports which can be quickly created, neutralising costs and boosting the ROI.

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Harriet Bowyer

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Yseop is an international AI software company and one of the early pioneers of natural language generation technology. Its proprietary AI platform, powers enterprise-level customer support, sales and reporting applications. Its highly customisable solutions deliver superior quality analysis and more human-like dialog, resulting in higher levels of customer satisfaction. Headquartered in France, Yseop has operations throughout Europe, the US and Asia.